1. Modeling SNS Availability Using BlockSim Geoffrey Milanovich Spallation Neutron Source

2. 2Managed by UT-Battelle for the U.S. Department of Energy Contributors G. Dodson for initial RAMI study and discussions Vivian Chang (summer student from Carnegie Mellon) for building much of the accelerator RBD S. Passmore for reliability data

3. 3Managed by UT-Battelle for the U.S. Department of Energy Outline SNS Availability Numbers Modeling an Entire Accelerator in BlockSim Possible uses of Reliability Model

4. 4Managed by UT-Battelle for the U.S. Department of Energy Presentation_name Downtime by Fiscal Year (07-15) comparison

5. 5Managed by UT-Battelle for the U.S. Department of Energy Presentation_name Downtime by Fiscal Year (07-15) comparison

6. 6Managed by UT-Battelle for the U.S. Department of Energy NP availability by week

7. 7Managed by UT-Battelle for the U.S. Department of Energy Down Time – Pareto Chart for FY16 to date

8. 8Managed by UT-Battelle for the U.S. Department of Energy In February 2016, the accelerator experienced 50 faults of 0.3 hours or more that affected neutron production 12.2 hrs. for (1) water leak into RFQ Xmtr oil tank 11.3 hrs. for (16) CCL 4 Window 1 vacuum faults and associated NEG pump regen 7.8 hrs. for (1) CCL Mod 2 smoke alarm/tank swap, and C phase IGBT failure 8.7 hrs. for (14) Ion Source plasma outages/Edmp faults/arcing events & conditioning 6.7 hrs. for (1) failed Group 3 controller in Ion Source Big Blue Box 4.4 hrs. for (13) RFQ out of closed loop faults 2.8 hrs. for (1) failed SCL LLRF oscillator 2.5 hrs. for (3) BL4B IPPS communication faults 2.5 hrs. for (1) DTL Mod 5 oil temperature fault 2.0 hrs. for (1) Ion source water leak 1.9 hrs. for (2) SCL filament power supply failures 1.3 hrs. for (1) instance of excessive beam loss; suspected RF issue Note that the graph and down time table above represent all faults for beam operation – neutron production and accelerator physics.

9. 9Managed by UT-Battelle for the U.S. Department of Energy Reliability Tracking – Metrics “Machine Health Report” weekly – prepared by Ops – Tracks chronic long term problems Traditional Metrics weekly Reviews of any events > 4 hours So far, it has been obvious when a system needs help

10. 10Managed by UT-Battelle for the U.S. Department of Energy Accelerator Improvement Projects HVCM upgrades – 400 hours downtime to 70 hours Warm linac vacuum change from ion pumps to turbo pumps – every warm RF trip damages SCL and possibly is 20 minutes DT MPS improvements – reduce delays to reduce # bad pulses Superconducting Linac plasma processing

11. 11Managed by UT-Battelle for the U.S. Department of Energy Non-AIP Improvements Errant beam tuning – Reduce frequency and effect of bad pulses Ion source continual improvement – 100 hrs DT to 40 Target development – mostly infant mortality, think we reached actual end of life on last target

12. 12Managed by UT-Battelle for the U.S. Department of Energy Reliability Estimates, in the Beginning “the spreadsheet” – numbers from industry, design specs, LANL, JLab, experts, and guesses Exponential distributions only (constant failure rate) For 160 hours mission time, availability: – Source – 87% – Linac – 80% – Modulators – 99%

13. 13Managed by UT-Battelle for the U.S. Department of Energy RBD/BlockSim Advantages Reliability Block Diagram (RBD) – “graphical representation of the components of the system and how they are reliability-wise related” Many failure distributions allowed Standby, load share, failure of switching to standby Analytical solution of very complex diagrams (but not an accelerator) Monte Carlo simulation – maintenance phases, spares, crews

14. 14Managed by UT-Battelle for the U.S. Department of Energy BlockSim 7/8 Model

15. 15Managed by UT-Battelle for the U.S. Department of Energy Ion Source

16. 16Managed by UT-Battelle for the U.S. Department of Energy RF Systems

17. 17Managed by UT-Battelle for the U.S. Department of Energy Standby Containers

18. 18Managed by UT-Battelle for the U.S. Department of Energy Full Accelerator Simulation Results Used exponential distributions (random failures) 86% - corrector power supplies, circulators, PPS modules, water valves, JT valves, etc. Change values for PS controllers and circulators from 50k to 100k 89% - RF feedthroughs, JT valves, etc. Change RF feedthrough from 100k to 4m (1 every 7 years), JT valves from 87k to 400k (1 every 5 years), etc 92% - Finally! But only for 1 seed

19. 19Managed by UT-Battelle for the U.S. Department of Energy Possible Uses of RBD Major component upgrade cost/benefits analysis – e.g. hot spare water pumps Compare different AIP proposals Planning maintenance cycles, if you have good data

20. 20Managed by UT-Battelle for the U.S. Department of Energy Conclusions SNS is middle aged - how long will it last? We still can not predict the wear-out curve – until it begins! But we can model maintenance plans RBD could be useful for large capital projects or long term scheduling, not so much for day to day running